Distributed networks of wireless sensors have been developed to provide information about remote objects and locations. For example, wireless sensor networks provide distributed remote access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. Sensor networks provide monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, military, and safety and security. Characteristics of wireless network sensors include small size, low power requirements, and one or more sensing and communication capabilities. These sensors are typically required to provide low power signal processing, low power computation, and low power, low cost wireless networking capability in a compact system.
Many tiny embedded sensor devices, equipped with various sensing and communication capabilities, form an ad-hoc network. The sensors can collaborate, exchange information, and report information outside of the network. For example, sensors embedded in cargo shipments can monitor conditions at the location of the cargo, such as temperature and humidity. For the sensed information to be most useful, it is usually necessary for the location of the sensor to be available. Therefore, a sensor is typically capable of localization, or determining its location in space. In addition to reporting the location of sensed information, localization is needed for facilitating routing in large ad-hoc networks. Localization is also required if it is desired to collect network survey information that can be used to study and characterize the network as a whole.
The incorporation of localization in wireless sensor networks is a complex undertaking. Sensor networks can be quite large and consist of sensors deployed in an ad-hoc fashion. In addition, the inherent constraints of the sensors themselves (including small size, low power consumption, and low cost) make it impractical to supply individual sensors with significant resources dedicated to localization. Localization becomes even more challenging when it is required to provide good location accuracy using only the signal strength of radio frequency (RF) signals. Many localization techniques using received signal strength (RSS) of RF signals have been proposed over years. Examples of such localization techniques are described in: [1] “Ecolocation: A Sequence Based Technique for RF Localization in Wireless Sensor Networks”, Kiran Yedavalli, Bhaskar Krishnamachari, Sharmila Ravula, Bhaskar Srinivasan, The Fourth International Conference on Information Processing in Sensor Networks (IPSN '05), Los Angeles, Calif., April 2005; [2] “Determination Using IEEE 802.11b”, Master of Science Thesis, Kiran Yedavalli, University of Colorado, Boulder, December 2002; [3] “Range-Free Localization Schemes for Large Scale Sensor Networks”, T. He, C. Huang, B. M. Blum, J. A. Stankovic and T. Abdelzaher, MobiCom '03, San Diego, Calif., September 2003; and [4] “Dynamic Fine Grained Localization in Ad-Hoc Sensor Networks”, A. Savvides, C. C. Han and M. B, Proceedings of the Fifth International Conference on Mobile Computing and Networking, Mobicom 2001, pp. 166-179, Rome, Italy, July 2001.
The relative performance of exiting RSS techniques depends on various location area conditions. Therefore, one technique may provide acceptable accuracy under particular location conditions, but unacceptable accuracy under different location conditions. FIGS. 1A, 1B, 1C, and 1D illustrate how location area conditions affect location technique accuracy. FIGS. 1A and 1B show results of location experiments conducted outdoors. FIGS. 1C and 1D show results of location experiments conducted indoors. All of the experiments were conducted using Berkeley MICA 2 motes. A mote is a wireless receiver/transmitter that is typically combined with a sensor of some type to create a remote sensor. Some motes are designed to be incredibly small so that they can be deployed by the hundreds or even thousands for various applications. Three different localization techniques—Ecolocation [1], Maximum Likelihood Estimator (MLE) [2] and Proximity [1]—are compared and for each location point.
Referring to FIG. 1A, in the outdoor experiment, a comparison was made between true locations and Ecolocation location estimates. 11 MICA 2 motes, placed randomly in a 144 sq. meters area, were used as reference nodes as well as unknown nodes. Consequently, each unknown node had 10 reference nodes. FIG. 1B shows the location error due to Ecolocation, MLE and Proximity for the outdoor experiment.
Referring to FIG. 1C, a comparison between true path and Ecolocation estimated path was made in the indoor experiment. 12 MICA 2 motes, placed randomly in a 120 sq. meters area, were used as reference nodes. The location of the unknown node was estimated for 5 different locations using the 12 reference nodes. FIG. 1D shows the location error due to Ecolocation, MLE and Proximity for the indoor experiment.
It can be seen that, even though Ecolocation performs the best for most of the cases, for some points other techniques provide better accuracy.